A comparison of geometric-and regression-based mobile gaze-tracking

Abstract

Video-based gaze-tracking systems are typically restricted in terms of their effective tracking space. This constraint limits the use of eyetrackers in studying mobile human behavior. Here, we compare two possible approaches for estimating the gaze of participants who are free to walk in a large space whilst looking at different regions of a large display. Geometrically, we linearly combined eye-in-head rotations and head-in-world coordinates to derive a gaze vector and its intersection with a planar display, by relying on the use of a head-mounted eyetracker and body-motion tracker. Alternatively, we employed Gaussian process regression to estimate the gaze intersection directly from the input data itself. Our evaluation of both methods indicates that a regression approach can deliver comparable results to a geometric approach. The regression approach is favored, given that it has the potential for further optimization, provides confidence bounds for its gaze estimates and offers greater flexibility in its implementation. Open-source software for the methods reported here is also provided for user implementation.